Digital Commons Network^™

Blockchain In Smart Cities, Shamma Alnuaimi

Thesis/ Dissertation Defenses

This thesis explores the integration of blockchain technology with smart cities to enhance efficiency, transparency, and reliability. The basic principles of blockchain, its layered architecture, consensus protocols, and decentralized security are first reviewed. The thesis then highlights how blockchain features can be integrated with urban and energy systems. The research focuses on how token economy can be used with smart cities to promote individuals to engage in desired behaviors by employing blockchain-based incentive mechanisms that can encourage responsible energy consumption.

The FairChain test system was developed and tested using blockchain technology and the Internet of Things (IoT) to demonstrate the …

A Feature-Free Deep Learning Approach For Midair Hand Gesture Recognition From Surface Electromyogram (Semg) Data, Yasir Altaf, Abdul Wahid, Mudasir Manzoor Kirmani

Turkish Journal of Electrical Engineering and Computer Sciences

Midair hand gesture recognition plays a crucial role in applications such as sign language recognition and human-computer interaction, particularly for supporting individuals with partial or complete hearing loss. However, recognizing gestures in midair remains challenging due to the rapid and complex nature of hand movements. To address this, noninvasive techniques like surface electromyography (sEMG)—which captures muscle activity through sensors placed on the skin—have gained attention. sEMG provides rich time-series data that reflect both spatial and temporal muscle dynamics. In this study, we propose a deep learning architecture that combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to classify …

Integrated Log Spectrogram Convolutional Neural Network (Ils-Cnn) For Robust Spoken Digit Recognition, Awais Ahmed

Turkish Journal of Electrical Engineering and Computer Sciences

Spoken digit recognition (SDR), a type of supervised automatic speech recognition, is essential for various human-machine interaction applications, including banking operations, dialing systems, price extraction, and airline reservation systems. However, designing an effective SDR system presents several challenges, such as developing labeled audio data, selecting appropriate feature extraction methods, and creating high-performance models. To overcome these challenges, a novel approach for robust spoken digit recognition using an integrated log spectrogram convolutional neural network (ILS-CNN) has been proposed. The proposed work presents an efficient SDR method by taking advantage of a log spectrogram layer directly within the neural network to enhance …

Railway Track Condition Monitoring Based On Sensor Data And Artificial Neural Networks, Ivan Kots, Alina Isaeva, Mark Denisenko, Alexander Sinyukin, Andrey Kovalev

Turkish Journal of Electrical Engineering and Computer Sciences

Monitoring the condition of engineering objects is one of the urgent tasks of industry, construction, and transport infrastructure. This article describes a system for condition monitoring and diagnostics of rail tracks in real time. Compared with other similar studies, the proposed system has the advantages of compactness, usability, scalability and versatility of application. The proposed monitoring system is based on an Nvidia Jetson Nano embedded computing board and also includes inertial sensor modules, a microphone, a geolocation module, communication modules, an SSD storage device, and a battery. The prototype of the diagnostic module is a portable device that can be …

Modeling And Simulation Of Dynamic Energy Management Systems For Smart Buildings, Onur Özel, Ali̇ Rifat Boynueğri̇, Hayri̇ Yi̇ği̇t, Burak Tekgün

Turkish Journal of Electrical Engineering and Computer Sciences

This study presents a dynamic energy management system tailored for smart residential buildings, integrating thermal and electrical models to achieve both natural gas and electricity bill cost reduction. By harnessing wind and solar energy sources, the system aims to meet the diverse energy needs of modern homes. Through load shifting and thermal storage strategies, known as power-to-heat (P2H) approaches, the system ensures efficient renewable energy utilization while maintaining resident comfort. Validation of the proposed system was conducted using real-world data from the Yıldız Technical University Smart Home Laboratory, demonstrating its practical applicability and effectiveness. Results indicate significant reductions in both …

Grey Wolf Optimization Of Pi Controller For Power Management In Wind Farms: A Novel Approach, Anis Feddaoui, Lotfi Farah, Abdelouahab Benretem, Mohammed Abdeldjalil Djehaf

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes a novel power management strategy for wind farms using a grey wolf optimization (GWO)-based PI controller. The method aims to enhance active and reactive power control in systems employing dou bly fed induction generators. Three control strategies are evaluated—namely, a classical frequency-domain PI controller, an Artificial Neural Network (ANN)-based controller, and the proposed GWO-based PI controller—the last of which represents the main contribution. The classical PI and ANN controllers are included strictly for comparative bench marking. MATLAB simulations demonstrate that the GWO-beased PI controller offers superior dynamic performance, particularly in settling time and overshoot reduction. A power …

Performance Analysis Of Ris-Empowered Ofdm-Im Communications Under Weibull Fading And Joint Tx/Rx I/Q Imbalance, Büşra Ceni̇kli̇oğlu, İbrahi̇m Develi̇, Ayşe Eli̇f Canbi̇len

Turkish Journal of Electrical Engineering and Computer Sciences

A modernist technique, reconfigurable intelligent surface (RIS) provides outstanding signal reflection and amplification, making it highly valuable for upcoming communication systems. Besides, a major contributor is index modulation (IM), attaining superior spectral and energy efficiency, and achieving hardware sufficiency. The primary and novel contribution of this work is the derivation of a highly accurate, closed-form approximate expression for the average bit error rate (ABER) of an orthogonal frequency division multiplexing (OFDM)-IM system operating in the complex and challenging environment characterized by joint transmitter/receiver (Tx/Rx) in-phase and quadrature phase imbalance (IQI) and Weibull fading. This essential analytical achievement is facilitated by …

Fpga-Based Takagi-Sugeno Fuzzy Controller For Quadrotor Uav Stabilization And Trajectory Tracking, Hocine Khati, Mohamed Amine Nehmar, Arezki Fekik, Mohand Achour Touat, Hand Talem, Rabah Mellah

Turkish Journal of Electrical Engineering and Computer Sciences

This study presents the implementation of a fuzzy logic–based control system on a field-programmable gate array (FPGA) for a quadrotor autonomous aerial vehicle (UAV). The objective is to design and integrate six Takagi–Sugeno fuzzy controllers to regulate roll, pitch, and yaw angles, along with longitudinal, latitudinal, and altitude movements, thereby stabilizing the UAV and enabling it to follow a desired trajectory. Due to the computational complexity of the six controllers, achieving the desired performance requires considerable processing time, which can adversely affect the quadrotor’s mission. Owing to their high processing power and operating frequency, FPGAs enable the control algorithm to …

Exploring The Link Between Emotional States And Coding Task Quality: A Pilot Study, Aquib Reshad, Valentina Nino, Maria Valero, Adriane Randolph, Yang Shi

Faculty Articles

Emotions play a crucial role in shaping cognitive performance, yet their influence on programing remains understudied. This pilot study investigates the relationship between emotional states and coding task quality. Ten participants completed a programing task while their brain activity was recorded using electroencephalography (EEG), with frontal alpha asymmetry (FAI) applied as a neural marker of emotional valence. Emotional self-reports were collected using the Scale of Positive and Negative Experience (SPANE), and coding quality was evaluated through a structured rubric. Preliminary findings indicate a potential association between FAI and coding performance, whereas self-reported affect showed weaker or inconsistent patterns. Given the …

Towards A Generalized And Optimized Apriori Approach, Artem Abdikov

Master's Theses

Apriori is a machine learning algorithm developed in 1994 by R. Agrawal and R. Srikant for association rule mining purposes. This family of algorithms takes transactional data and analyzes relationships between variables in large datasets. The typical output of such algorithms is a prediction that if users choose item X, it is highly likely that they will also choose item Y. Apriori is known to be a robust algorithm and is used by many large companies in order to analyze user tendencies and even make recommendations. Although Apriori is a powerful algorithm, its original implementation is known to have limitations, …

Predicting Simulation Times For Multiphase Thermal-Hydraulic Models, Andrew Yule, Andrew Taylor

SMU Data Science Review

Addressing the challenge of computationally intensive OLGA

simulations in the oil and gas industry, a machine learning framework is

developed for accurate runtime prediction. A specialized feature extraction

pipeline identifies key parameters—such as simulation time, time step,

number of branches, and section count—from OLGA input files that serve as

high-impact predictors. Multiple predictive models, including regression,

tree-based ensembles, and neural networks, are implemented to validate

accuracy and robustness. Results reveal that prioritizing simulations based on

predicted runtimes optimizes licensing resources and reduces operational

costs, making real-time scheduling more efficient. This research demonstrates

the effectiveness of data-driven runtime prediction in enhancing …

Ai-Driven Optimization Of Wind Energy Distribution In Texas Using Multi-Agent Reinforcement Learning, Waleed Amer, Owolabi Oluwadamilola, Bassey Ogbonnaya

SMU Data Science Review

Abstract. The integration of large-scale wind power into modern electrical grids presents persistent challenges due to variability, curtailment, and compliance with operational constraints. This study proposes a multi-agent reinforcement learning (MARL) framework for optimizing wind energy distribution within the Texas power grid. The system employs three specialized agents—managing wind curtailment, storage utilization, and load adjustments—to collaboratively balance supply and demand under dynamic grid conditions. Using historical operational data from the Electric Reliability Council of Texas (ERCOT), the framework was trained and evaluated on a range of scenarios encompassing both typical and extreme operating conditions. Results demonstrate substantial performance improvements compared …

Development Of Composite Membranes And Nanofluid Absorbents Incorporating Surface-Functionalized Nanoparticles For Enhanced Co₂ Absorption In Gas–Liquid Membrane Contactors, Riya Ahammed Labeeb

Thesis/ Dissertation Defenses

Natural gas sweetening demands the efficient removal of CO₂ to enhance heating value and prevent equipment corrosion. Gas–liquid membrane contactors (GLMCs) have garnered attention as promising solutions to traditional separation methods due to their modularity, high interfacial area, and energy efficiency. Nonetheless, they suffer from some serious challenges such as membrane wetting, while absorbents either exhibit low absorption capacity or high energy demands and corrosion. This work addresses these limitations through two complementary strategies. In the first, ZnO nanoparticles were hydrophobically functionalised and incorporated into polymer matrices to fabricate composite membranes with superior hydrophobicity and reduced pore wetting. In the …

Bio-Corrosion Studies On Novel Zr‑Co‑Ti Based Metallic Glass Alloys For Biomedical Implant Applications, Shubhra Shitole

Thesis/ Dissertation Defenses

The relentless pursuit of advanced biomaterials that synergistically combine high mechanical strength, exceptional corrosion resistance, and inherent biocompatibility is critical for next-generation medical implants. This research addresses this challenge through the development and multi-faceted evaluation of a novel library of Zr-Co-Ti-based metallic glasses (MGs); Zr₆₀Co₃₀Ti₁₀, Zr₅₅Co₃₅Ti₁₀, and Zr₅₀Co₄₀Ti₁₀, fabricated via melt-spinning. The alloys were confirmed to be fully amorphous by X-ray diffraction (XRD), a structure underpinned by exceptional thermal stability, with the Zr60 composition exhibiting a larger supercooled liquid region (Δ𝑇𝑥) of …

Durability Of Basalt Fiber-Reinforced Polymer Bars In Moist Concrete Under Sustained Load, Osama Ahmed

Thesis/ Dissertation Defenses

This thesis is concerned with the long-term durability of basalt fiber-reinforced polymer (BFRP) bars embedded in moist concrete. The study focuses on the combined effects of exposure duration, elevated temperature, and sustained tensile stress, which together influence the mechanical and chemical stability of BFRP reinforcement in aggressive environments. The main objective is to evaluate the degradation mechanisms of BFRP bars subjected to coupled hygrothermal and mechanical conditions and to develop a predictive durability model capable of estimating service life under realistic climate scenarios. Concrete-encased BFRP bars were conditioned at three temperatures (20, 40, and 60°C), three durations (3, 6, and …

Assessment Of Traffic Related Air Pollution Effects On Indoor Air Quality In Educational Buildings, Ahmed Gouda Mohamed, Mohamed Sherif Dr., Fahad Alqahtani Dr., Yousif Deeb Eng.

Civil Engineering

This study assesses the impact of traffic-related air pollution (TRAP) on indoor air quality (IAQ) within a Riyadh school. The main objectives are to pinpoint factors influencing IAQ and to evaluate the effectiveness of existing ventilation and filtration systems. Air quality Egg sensors monitored nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter (PM1.0, PM2.5, PM10) levels both indoors and outdoors over 30 days. Findings revealed average indoor PM2.5 and PM10 concentrations of 14.09 µg/m3 and 18.15 µg/m3, respectively, while outdoor concentrations averaged 20.63 µg/m3 and 27.88 µg/m3. A strong positive correlation was observed between indoor and outdoor PM levels, …

Diffog: Differentiable Policy Trajectory Optimization With Generalizability, Zhengtong Xu, Zichen Miao, Qiang Qiu, Zhe Zhang, Yu She

School of Industrial Engineering Faculty Publications

Imitation-learning-based visuomotor policies excel at manipulation tasks but often produce suboptimal action trajectories compared to model-based methods. Directly mapping camera data to actions via neural networks can result in jerky motions and difficulties in meeting critical constraints, compromising safety and robustness in real-world deployment. For tasks that require high robustness or strict adherence to constraints, ensuring trajectory quality is crucial. However, the lack of interpretability in neural networks makes it challenging to generate constraint-compliant actions in a controlled manner. This article introduces differentiable policy trajectory optimization with generalizability (DiffOG), a learning-based trajectory optimization framework designed to enhance visuomotor policies. By …

Energy Management Of Battery Supercapacitor Hybrid Storage In Electric Vehicles With Solar Integration: Review, Islam Sayed, Yousef Mahmoud

Faculty Articles

Hybrid energy storage systems (HESS) integrating batteries and supercapacitors offer a promising solution to overcome the limitations of battery-only architectures in electric vehicles (EVs). By leveraging the high energy density of batteries and the high power density of supercapacitors, HESS can enhance power delivery, improve energy efficiency, and extend battery lifespan. The effectiveness of HESS, however, is largely determined by the energy management system (EMS) that coordinates power flow under dynamic driving conditions. Unlike existing reviews, this work addresses common gaps in the literature, including the lack of industrial energy storage component examples, limited coverage of recently developed EMS strategies, …

Effect Of Pore Structure On Void Formation In Lotus Type Porous Cu/Solder Joints, Jin-Kwan Lee, Keun-Soo Kim, Jae-Ho Shin, Seung-Min Cho, Sung Yi, Sang-Wook Kim, Soong-Keun Hyun

Mechanical and Materials Engineering Faculty Publications and Presentations

Void formation in solder joints is a critical reliability challenge in high-power electronics, as it degrades thermal dissipation and mechanical integrity. This study investigates a novel structural approach to mitigate this issue. The influence of an uni-directional porous structure in lotus-type porous Cu (Louts Cu) on void formation in solder joints was investigated. All pores in lotus Cu were infiltrated with SAC305 (Sn–3.0Ag–0.5Cu) solder paste. Then reflow soldering was performed under three different atmospheric conditions: air, nitrogen, and vacuum. The microstructure and void fraction were characterized. The shear strength was evaluated. The shear strength of Louts Cu joint was slightly …

2025 - The Sixth Annual Fall Symposium Of Student Scholars

Symposium of Student Scholars Program Books

The full program book from the Fall 2025 Symposium of Student Scholars, held in November 2025. Includes abstracts from the presentations and posters.

Field Canals Improvement Projects Duration Prediction: A Comparative Analysis Of Machine Learning Models, Hania Ghouse, Ukaegbu Chinonso Ishmael, Edgar Dario Obando-Paredes, Hashem Shafik Shakir, Ali Al-Bayaty

Electrical and Computer Engineering Faculty Publications and Presentations

There are several essential elements in project construction management to be studied appropriately, and priority to these elements, such as cost and duration, is predominantly interesting to be investigated. In this research, the duration of field canal improvement projects (DFCIP) was predicted using two relatively new machine learning (ML) models - the Multivariate Adaptive Regression Spline (MARS) and Extreme Learning Machine (ELM). The targeted DFCIP was calculated using other dependent parameters, such as the length of the pipe, years of construction, the geographical zone of the network, the supplied area with water, and finally the actual cost of the field …

Yolo-Based Marine Search And Rescue Using Uav Multi-Dataset In Challenging Weather Conditions, Aysha Alshebli

Thesis/ Dissertation Defenses

Object detection models, powered by deep learning and computer vision, are revolutionizing marine search and rescue (SAR). By analyzing aerial imagery and live drone footage, these systems automatically identify critical targets like survivors, life rafts, and debris across vast and treacherous ocean areas. This capability enhances operational efficiency by reducing human workload and accelerating response times, even in challenging conditions such as poor light, high seas, or cluttered backgrounds. The result is continuous monitoring, faster decision-making, and a significantly improved probability of successful rescue.

Departing from prior methodologies, YOLO introduced a paradigm shift through its single-shot architecture, which concurrently predicts …

Investigate The Performance And Airtightness Of Short Carbon Fiber Reinforced Polypropylene Pressure Vessel Fabricated Via Fdm 3d Printing, Trad Abdallah Abualbandora

Thesis/ Dissertation Defenses

This work offers a thorough investigation into the performance of 3D-printed, short carbon fiber-reinforced polypropylene pressure vessels, specifically designed for gas storage in demanding sectors like aerospace, drones, and medical equipment, operating within a pressure range of 0-10 bar. A key challenge in FDM additive manufacturing of pressure vessels is achieving airtightness due to the inherent porosity and micro-gaps resulting from the printing process. To address this, the fabricated vessel models were coated with various commercially available materials to effectively fill these microscopic imperfections and ensure the necessary tightness. Spray coating and adhesion coating materials were applied to the outer …

Notes For Contributors, Editors Space And Defense

Space and Defense

No abstract provided.

Review Article: International Relations: State Of The Field. Paul R. Viotti’S Kenneth Waltz: An Intellectual Biography (Ny: Columbia University Press, 2023)., Damon Coletta

Space and Defense

In this review essay, Damon Coletta examines Paul R. Viotti’s Kenneth Waltz: An Intellectual Biography (Columbia University Press, 2023), positioning it as both a tribute to and a reexamination of one of International Relations’ most influential theorists. Viotti, a former student of Waltz, reconstructs the intellectual development of structural realism—tracing its philosophical and methodological roots from Man, the State, and War (1959) to Theory of International Politics (1979)—while situating Waltz’s scholarship within the geopolitical and academic transformations of the Cold War era. Coletta highlights Viotti’s skillful balance between biography and disciplinary analysis, showing how Waltz’s systemic approach to international politics …

Review Article: Monetary Power And National Security. A 10th Anniversary Review Of Paul R. Viotti’S The Dollar And National Security: The Monetary Component Of Hard Power (Stanford, Ca: Stanford University Press, 2014), Ricardo Crespo

Space and Defense

In this ten-year retrospective, Ricardo A. Crespo reassesses Paul R. Viotti’s The Dollar and National Security (2014) as a foundational text bridging the fields of monetary policy and national security. Viotti’s central thesis—that military power ultimately depends on a stable and privileged monetary foundation—has only grown more relevant amid today’s weaponization of finance and the geopolitics of sanctions. Crespo highlights Viotti’s argument that sustaining the U.S. dollar’s global dominance requires cooperative security and international consensus, noting how historical parallels—from the sterling gold standard to Bretton Woods—illustrate the fragility of monetary hegemony under fiscal mismanagement or political fragmentation. The review situates …

Plan To Fail By Failing To Plan: Deterring Prc Nuclear Force Expansion, Mark Tang, Grace Buettner

Space and Defense

This article argues that the United States must adapt its nuclear deterrence strategy to counter the People’s Republic of China’s (PRC) accelerating nuclear force expansion. As Beijing seeks to achieve nuclear parity with Washington—projecting up to 1,500 warheads by 2035—the authors contend that U.S. strategy must evolve from Cold War-era models to meet the Indo-Pacific’s unique strategic challenges. They propose the establishment of a Pacific Air Forces (PACAF) A-10 Strategic Deterrence and Nuclear Integration Division, modeled after its European counterpart, to enhance regional nuclear planning, integration, and allied assurance. The paper emphasizes the concept of escalation domination, arguing …

Earth’S Orbital Prison: Codifying Customary Law To Prevent Environmental Catastrophe In Space, Jacob Cook

Space and Defense

This article argues that the accelerating proliferation of space debris in low Earth orbit (LEO) poses an existential threat to the sustainability of the orbital environment—one comparable in strategic gravity to nuclear deterrence during the Cold War. Drawing on liberal internationalist theory and the concept of Mutually Assured Destruction (MAD), the paper asserts that deliberate satellite destruction through anti-satellite (ASAT) testing and negligent debris generation risk triggering a self-sustaining cascade of collisions known as the Kessler Syndrome. Existing treaties, including the Outer Space Treaty and the Artemis Accords, lack enforceable mechanisms to prevent such catastrophic harm. To fill this …

Enabling The Decisive Advantage, Mallory Stewart

Space and Defense

In her keynote address at the 2025 USSPACECOM Legal Conference, former Assistant Secretary of State Mallory Stewart argues that law, norms, and international cooperation are central to maintaining the United States’ “decisive advantage” in outer space. She emphasizes that rules and standards—far from constraining innovation or strategic flexibility—enhance predictability, reduce risk, and empower responsible competition. Stewart highlights the success of initiatives such as the Artemis Accords, the U.S. commitment to forgo destructive direct-ascent anti-satellite (ASAT) tests, and the global expansion of Space Situational Awareness (SSA) agreements as examples of how legal and normative frameworks reinforce stability and collaboration. She …

Space, Law, And National Security, Stephen Whiting

Space and Defense

In his address to the 2025 USSPACECOM Legal Conference, General Stephen Whiting underscores the indispensable role of legal professionals in shaping the future of military space operations and maintaining the rules-based international order in an increasingly contested domain. He outlines the rapid transformation of the space environment driven by commercial innovation, technological proliferation, and the accelerating threat landscape posed by adversaries such as China and Russia. The speech highlights U.S. Space Command’s three “moral responsibilities”: ensuring the delivery of space capabilities to the joint force and allies, protecting and defending critical space assets, and safeguarding the joint force from space-enabled …